CN112070701A - Image generation method, device, equipment and computer readable medium - Google Patents
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Abstract
The embodiment of the disclosure discloses an image generation method, an image generation device, an electronic device and a computer readable medium. One embodiment of the method comprises: down-sampling the original image to obtain a reduced image; generating at least one defogged image information based on the reduced image; and generating a defogged image based on the at least one defogged image information and the reduced image. In the embodiment, the reduced image obtained by down-sampling the original image contains fewer pixel points than the original image, so that the defogged image information is extracted by analyzing the reduced image instead of the original image directly, the calculated amount in the analysis process is reduced, and the defogging speed is increased.
Description
Technical Field
Embodiments of the present disclosure relate to the field of computer technologies, and in particular, to an image generation method, apparatus, device, and computer-readable medium.
Background
Among the image processing techniques, there is an image defogging technique which desires to restore the contrast of an original image whose contrast is lower than a preset threshold value to make the original image clear to visually present the defogging effect. The existing image defogging technology needs a large amount of calculation, and further causes long time consumption and low defogging speed.
Disclosure of Invention
This summary is provided to introduce a selection of concepts in a simplified form that are further described below in the detailed description. This summary is not intended to identify key features or essential features of the claimed subject matter, nor is it intended to be used to limit the scope of the claimed subject matter.
Some embodiments of the present disclosure propose an image generation method, apparatus, device and computer readable medium to solve the technical problems mentioned in the background section above.
In a first aspect, some embodiments of the present disclosure provide an image generation method, the method comprising: down-sampling the original image to obtain a reduced image; generating at least one defogged image information based on the reduced image; and generating a defogged image based on the at least one defogged image information and the reduced image.
In a second aspect, some embodiments of the present disclosure provide an image generation apparatus, the apparatus comprising: a down-sampling unit configured to down-sample the original image to obtain a reduced image; a first generating unit configured to generate at least one defogged image information based on the reduced image; a second generating unit configured to generate a defogged image based on the at least one defogged image information and the reduced image.
In a third aspect, some embodiments of the present disclosure provide an electronic device, comprising: one or more processors; a storage device having one or more programs stored thereon, which when executed by one or more processors, cause the one or more processors to implement the method as described in any of the implementations of the first aspect.
In a fourth aspect, some embodiments of the disclosure provide a computer readable medium having a computer program stored thereon, where the program when executed by a processor implements a method as described in any of the implementations of the first aspect.
One of the above-described various embodiments of the present disclosure has the following advantageous effects: because the reduced image obtained by down-sampling the original image contains fewer pixel points than the original image, the defogged image information is extracted by analyzing the reduced image instead of the original image directly, so that the calculated amount in the analysis process is reduced, and the defogging speed is increased.
Drawings
The above and other features, advantages and aspects of various embodiments of the present disclosure will become more apparent by referring to the following detailed description when taken in conjunction with the accompanying drawings. Throughout the drawings, the same or similar reference numbers refer to the same or similar elements. It should be understood that the drawings are schematic and that elements and elements are not necessarily drawn to scale.
FIG. 1 is a schematic illustration of one application scenario of the image generation method of some embodiments of the present disclosure;
FIG. 2 is a flow diagram of some embodiments of an image generation method according to the present disclosure;
FIG. 3 is a flow diagram of further embodiments of an image generation method according to the present disclosure;
FIG. 4 is a schematic structural diagram of some embodiments of an image generation apparatus according to the present disclosure;
FIG. 5 is a schematic structural diagram of an electronic device suitable for use in implementing some embodiments of the present disclosure.
Fig. 6 illustrates a schematic diagram of a down-sampling step of some embodiments of an image generation method according to the present disclosure.
Fig. 7 shows a schematic diagram of a step of generating a defogged image in an image generation method according to some embodiments of the present disclosure.
Detailed Description
Embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. While certain embodiments of the present disclosure are shown in the drawings, it is to be understood that the disclosure may be embodied in various forms and should not be construed as limited to the embodiments set forth herein. Rather, these embodiments are provided for a more thorough and complete understanding of the present disclosure. It should be understood that the drawings and embodiments of the disclosure are for illustration purposes only and are not intended to limit the scope of the disclosure.
It should be noted that, for convenience of description, only the portions related to the related invention are shown in the drawings. The embodiments and features of the embodiments in the present disclosure may be combined with each other without conflict.
It should be noted that the terms "first", "second", and the like in the present disclosure are only used for distinguishing different devices, modules or units, and are not used for limiting the order or interdependence relationship of the functions performed by the devices, modules or units.
It is noted that references to "a", "an", and "the" modifications in this disclosure are intended to be illustrative rather than limiting, and that those skilled in the art will recognize that "one or more" may be used unless the context clearly dictates otherwise.
The names of messages or information exchanged between devices in the embodiments of the present disclosure are for illustrative purposes only, and are not intended to limit the scope of the messages or information.
The present disclosure will be described in detail below with reference to the accompanying drawings in conjunction with embodiments.
Fig. 1 shows a schematic diagram of one application scenario in which the image generation method of some embodiments of the present disclosure may be applied.
In the application scenario shown in fig. 1, first, the computing device 101 may down-sample the original image 102, resulting in a reduced image 103. In the present application scenario, the original image includes 3 channels, which correspond to the three primary colors of red, blue, and green, respectively. The down-sampling method of the original image is shown as reference numeral 106, the size of the original image is 4 × 3, and the down-sampling of the original image is performed by 2 times, so that a reduced image with the size of 2 × 3 is obtained. And the pixel value of each pixel point in the reduced image is the pixel value of the pixel point at the upper left in the corresponding 2 x 2 window in the original image. Thereafter, at least one defogged image information 104 is generated based on the reduced image 103. In the application scenario, the at least one defogged image information 104 includes one defogged image information. And the defogged image information is expressed by a matrix with the same size as the reduced image. Finally, a defogged image 105 is generated based on the at least one defogged image information 104 and the reduced image 103. In this application scenario, the execution subject averages the at least one defogged image information 104 and the matrix form 107 of the reduced image by element to obtain an average result 108. Then, the average result 108 is up-sampled to obtain a matrix form 109 of the defogged image, and finally the defogged image 105 is obtained.
The computing device 101 may be hardware or software. When the computing device is hardware, it may be implemented as a distributed cluster composed of a plurality of servers or electronic devices, or may be implemented as a single server or a single electronic device. When the computing device is embodied as software, it may be implemented as multiple pieces of software or software modules, for example, to provide distributed services, or as a single piece of software or software module. And is not particularly limited herein.
It should be understood that the number of computing devices 101 in FIG. 1 is merely illustrative. There may be any number of computing devices 101, as desired for implementation.
With continued reference to fig. 2, a flow 200 of some embodiments of an image generation method according to the present disclosure is shown. The image generation method comprises the following steps:
In some embodiments, the original image may be any image with a contrast smaller than a preset threshold. For example, the image generation method may execute an image stored in the subject whose contrast is smaller than a preset threshold. As another example, an image whose contrast is smaller than a preset threshold value disclosed in the network may be used.
With further reference to fig. 6, fig. 6 shows a schematic diagram of a down-sampling step according to some embodiments of the image generation method of the present disclosure. As shown in fig. 6, in some embodiments, the down-sampling of the original image may be based on a size of the original image h × w × c, where h is a height of the original image, w is a width of the original image, and c is a number of channels of the original image. And performing s-fold down-sampling on the original image, wherein s can be any common divisor of h and w, and obtaining a reduced image with the size of each channel being (h/s) × (w/s) × c. And the pixel value of each pixel point in the reduced image is a value obtained by rounding the average value of elements contained in the corresponding s-s window in the original image.
In some embodiments, the execution subject may obtain at least one defogged image information by inputting the reduced image into an online image defogging tool or image defogging software.
In some optional implementations in some embodiments, the executing body may further obtain at least one piece of defogged image information by inputting the reduced image into an image defogging network.
In some embodiments, the defogged image information is in a matrix form. The sizes of the matrix in the length direction, the width direction and the height direction respectively correspond to the sizes of the reduced images in the length direction, the width direction and the channel number. The values of the elements in the matrix represent the pixel values of the reduced image after defogging.
In some embodiments, the image defogging network may be any network that defogges an input image to obtain a defogged image. By way of example, the image defogging Network may include, but is not limited to, a GCANet (Gated Context Aggregation Network), a DehazeNet (defogging Network), a domain adaptive image defogging Network, a Gated Fusion Network, and the like.
In some optional implementations of some embodiments, the image defogging network may include a first image defogging network and a second image defogging network. On this basis, the execution subject may first input the reduced image into the first image defogging network to obtain first defogged image information. And then, inputting the first defogged image information into the second image defogging network to obtain second defogged image information. And finally, determining the first defogged image information and the second defogged image information as the at least one piece of defogged image information.
In some embodiments, the first image defogging network or the second image defogging network may be any network that defogges an input image to obtain a defogged image. By way of example, the image defogging Network may include, but is not limited to, a GCANet (Gated Context Aggregation Network), a DehazeNet (defogging Network), a domain adaptive image defogging Network, a Gated Fusion Network, and the like.
In some embodiments, the step of obtaining the content indicated by reference numeral 109 is further obtained as the content indicated by reference numeral 107 and reference numeral 104 in fig. 1 is obtained by the content indicated by reference numeral 108. The execution body may average each of the at least one defogged image information and the matrix form of the reduced image by elements to obtain an average result. And then, performing up-sampling on the average result to obtain a defogged image.
In some optional implementations of some embodiments, with further reference to fig. 7, fig. 7 shows a schematic diagram of a step of generating a defogged image in an image generation method according to some embodiments of the present disclosure. As shown in fig. 7, the computing device 701 may add each of the above-described at least one defogged image information 702 and the above-described matrix form 703 of the reduced image in terms of elements, resulting in an addition result 704. In this embodiment, the at least one defogged image information includes one defogged image information. After that, the addition result 704 is convolved to obtain a convolution result 706. In the present embodiment, the convolution matrix is a 1 x 1 matrix with element sizes of 1/2, as indicated by reference numeral 705. Finally, the convolution result 706 is up-sampled to obtain a defogged image 707.
In some embodiments, the element in the reduced image is a value of each pixel in the reduced image in each channel.
According to the method provided by some embodiments of the disclosure, because the reduced image obtained by down-sampling the original image contains fewer pixel points than the original image, the defogged image information is extracted by analyzing the reduced image rather than the original image directly, so that the calculation amount in the analysis process is reduced, and the defogging speed is increased.
With further reference to fig. 3, a flow 300 of further embodiments of an image generation method is shown. The flow 300 of the image generation method includes the following steps:
In some embodiments, the specific implementation of step 301 and the technical effect thereof may refer to step 201 in the embodiment corresponding to fig. 2, and are not described herein again.
In some embodiments, the first image defogging network may be any network that defogges an input image to obtain a defogged image. By way of example, the image defogging Network may include, but is not limited to, a GCANet (Gated Context Aggregation Network), a DehazeNet (defogging Network), a domain adaptive image defogging Network, a Gated Fusion Network, and the like.
In some embodiments, the first defogged image information is in a matrix form. The sizes of the matrix in the length direction, the width direction and the height direction respectively correspond to the sizes of the reduced images in the length direction, the width direction and the channel number. The values of the elements in the matrix represent the pixel values of the reduced image after defogging.
In some embodiments, the second image defogging network may be any network that defogges an input image to obtain a defogged image. By way of example, the image defogging Network may include, but is not limited to, a GCANet (Gated Context Aggregation Network), a DehazeNet (defogging Network), a domain adaptive image defogging Network, a Gated Fusion Network, and the like.
In some embodiments, the second defogged image information is in a matrix form. The sizes of the matrix in the length direction, the width direction and the height direction respectively correspond to the sizes of the reduced images in the length direction, the width direction and the channel number. The values of the elements in the matrix represent the pixel values of the reduced image after defogging.
In some embodiments, the element in the reduced image is a value of each pixel in the reduced image in each channel.
And step 306, performing up-sampling on the addition result to obtain a defogged image.
In some embodiments, the execution subject may upsample the original image using any upsampling algorithm. By way of example, the above up-sampling algorithm may include, but is not limited to, the following algorithms: nearest neighbor interpolation, bilinear interpolation, bicubic interpolation, edge-based image interpolation, region-based image interpolation, and the like.
As can be seen from fig. 3, compared to the description of some embodiments corresponding to fig. 2, the flow 300 of the image generation method in some embodiments corresponding to fig. 3 embodies the steps of generating at least one defogging information and generating a defogged image. Therefore, the solutions described in the embodiments generate the first defogged image information, then generate the second defogged image information based on the first defogged image information, and generate the defogged image information by multiple accumulation, so as to extract more and more accurate defogged image information and further make the image defogging effect better. And the defogged image is generated through upsampling, so that the image can be quickly defogged and then restored to the original size.
With further reference to fig. 4, as an implementation of the methods illustrated in the above figures, the present disclosure provides some embodiments of an image generation apparatus, which correspond to those illustrated in fig. 2, and which may be particularly applicable in various electronic devices.
As shown in fig. 4, the image generation apparatus 400 of some embodiments includes: downsampling section 401, first generating section 402, and second generating section 403. Wherein, the down-sampling unit 401 is configured to down-sample the original image to obtain a reduced image; a first generating unit 402 configured to generate at least one defogged image information based on the reduced image; a second generating unit 403 configured to generate a defogged image based on the at least one defogged image information and the reduced image.
In an optional implementation of some embodiments, the first generating unit is further configured to: and inputting the reduced image into an image defogging network to obtain the at least one piece of defogged image information.
In an optional implementation of some embodiments, the image defogging network includes: a first image defogging network and a second image defogging network; and the first generating unit is further configured to: inputting the reduced image into the first image defogging network to obtain first defogged image information; inputting the first defogged image information into the second image defogging network to obtain second defogged image information; and determining the first defogged image information and the second defogged image information as the at least one defogged image information.
In an optional implementation of some embodiments, the second generating unit is further configured to: adding the first defogged image information, the second defogged image information and the matrix form of the reduced image in the at least one defogged image information according to elements to obtain an addition result; convolving the addition result to obtain a convolution result; and performing upsampling on the convolution result to obtain the defogged image.
It will be understood that the elements described in the apparatus 400 correspond to various steps in the method described with reference to fig. 2. Thus, the operations, features and resulting advantages described above with respect to the method are also applicable to the apparatus 400 and the units included therein, and will not be described herein again.
Referring now to fig. 5, a schematic diagram of an electronic device (e.g., the server or terminal device of fig. 1) 500 suitable for use in implementing some embodiments of the present disclosure is shown. The electronic device in some embodiments of the present disclosure may include, but is not limited to, a mobile terminal such as a mobile phone, a notebook computer, a digital broadcast receiver, a PDA (personal digital assistant), a PAD (tablet computer), a PMP (portable multimedia player), a vehicle-mounted terminal (e.g., a car navigation terminal), and the like, and a stationary terminal such as a digital TV, a desktop computer, and the like. The electronic device shown in fig. 5 is only an example, and should not bring any limitation to the functions and the scope of use of the embodiments of the present disclosure.
As shown in fig. 5, electronic device 500 may include a processing means (e.g., central processing unit, graphics processor, etc.) 501 that may perform various appropriate actions and processes in accordance with a program stored in a Read Only Memory (ROM)502 or a program loaded from a storage means 508 into a Random Access Memory (RAM) 503. In the RAM503, various programs and data necessary for the operation of the electronic apparatus 500 are also stored. The processing device 501, the ROM 502, and the RAM503 are connected to each other through a bus 504. An input/output (I/O) interface 505 is also connected to bus 504.
Generally, the following devices may be connected to the I/O interface 505: input devices 506 including, for example, a touch screen, touch pad, keyboard, mouse, camera, microphone, accelerometer, gyroscope, etc.; output devices 507 including, for example, a Liquid Crystal Display (LCD), speakers, vibrators, and the like; storage devices 508 including, for example, magnetic tape, hard disk, etc.; and a communication device 509. The communication means 509 may allow the electronic device 500 to communicate with other devices wirelessly or by wire to exchange data. While fig. 5 illustrates an electronic device 500 having various means, it is to be understood that not all illustrated means are required to be implemented or provided. More or fewer devices may alternatively be implemented or provided. Each block shown in fig. 5 may represent one device or may represent multiple devices as desired.
In particular, according to some embodiments of the present disclosure, the processes described above with reference to the flow diagrams may be implemented as computer software programs. For example, some embodiments of the present disclosure include a computer program product comprising a computer program embodied on a computer readable medium, the computer program comprising program code for performing the method illustrated in the flow chart. In some such embodiments, the computer program may be downloaded and installed from a network via the communication means 509, or installed from the storage means 508, or installed from the ROM 502. The computer program, when executed by the processing device 501, performs the above-described functions defined in the methods of some embodiments of the present disclosure.
It should be noted that the computer readable medium described in some embodiments of the present disclosure may be a computer readable signal medium or a computer readable storage medium or any combination of the two. A computer readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the foregoing. More specific examples of the computer readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In some embodiments of the disclosure, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. In some embodiments of the present disclosure, however, a computer readable signal medium may include a propagated data signal with computer readable program code embodied therein, for example, in baseband or as part of a carrier wave. Such a propagated data signal may take many forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A computer readable signal medium may also be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to: electrical wires, optical cables, RF (radio frequency), etc., or any suitable combination of the foregoing.
In some embodiments, the clients, servers may communicate using any currently known or future developed network Protocol, such as HTTP (HyperText Transfer Protocol), and may interconnect with any form or medium of digital data communication (e.g., a communications network). Examples of communication networks include a local area network ("LAN"), a wide area network ("WAN"), the Internet (e.g., the Internet), and peer-to-peer networks (e.g., ad hoc peer-to-peer networks), as well as any currently known or future developed network.
The computer readable medium may be embodied in the electronic device; or may exist separately without being assembled into the electronic device. The computer readable medium carries one or more programs which, when executed by the electronic device, cause the electronic device to: down-sampling the original image to obtain a reduced image; generating at least one defogged image information based on the reduced image; and generating a defogged image based on the at least one defogged image information and the reduced image.
Computer program code for carrying out operations for embodiments of the present disclosure may be written in any combination of one or more programming languages, including an object oriented programming language such as Java, Smalltalk, C + +, and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet service provider).
The flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present disclosure. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
The units described in some embodiments of the present disclosure may be implemented by software, and may also be implemented by hardware. The described units may also be provided in a processor, and may be described as: a processor includes a downsampling unit, a first generating unit, and a second generating unit. Where the names of the cells do not in some cases constitute a limitation on the cell itself, for example, the second generation cell may also be described as a "cell generating a defogged image".
The functions described herein above may be performed, at least in part, by one or more hardware logic components. For example, without limitation, exemplary types of hardware logic components that may be used include: field Programmable Gate Arrays (FPGAs), Application Specific Integrated Circuits (ASICs), Application Specific Standard Products (ASSPs), systems on a chip (SOCs), Complex Programmable Logic Devices (CPLDs), and the like.
According to one or more embodiments of the present disclosure, there is provided an image generation method including: down-sampling the original image to obtain a reduced image; generating at least one defogged image information based on the reduced image; and generating a defogged image based on the at least one defogged image information and the reduced image.
According to one or more embodiments of the present disclosure, generating at least one defogged image information based on the reduced image includes: and inputting the reduced image into an image defogging network to obtain the at least one piece of defogged image information.
According to one or more embodiments of the present disclosure, an image defogging network includes: a first image defogging network and a second image defogging network; and inputting the reduced image into an image defogging network to obtain the at least one defogged image information, comprising: inputting the reduced image into the first image defogging network to obtain first defogged image information; inputting the first defogged image information into the second image defogging network to obtain second defogged image information; and determining the first defogged image information and the second defogged image information as the at least one defogged image information.
According to one or more embodiments of the present disclosure, generating a defogged image based on the at least one defogged image information and the reduced image includes: adding the first defogged image information, the second defogged image information and the matrix form of the reduced image in the at least one defogged image information according to elements to obtain an addition result; convolving the addition result to obtain a convolution result; and performing upsampling on the convolution result to obtain the defogged image.
According to one or more embodiments of the present disclosure, there is provided an image generation apparatus including: a down-sampling unit configured to down-sample the original image to obtain a reduced image; a first generating unit configured to generate at least one defogged image information based on the reduced image; a second generating unit configured to generate a defogged image based on the at least one defogged image information and the reduced image.
According to one or more embodiments of the present disclosure, the first generating unit is further configured to: and inputting the reduced image into an image defogging network to obtain the at least one piece of defogged image information.
According to one or more embodiments of the present disclosure, an image defogging network includes: a first image defogging network and a second image defogging network; and the first generating unit is further configured to: inputting the reduced image into the first image defogging network to obtain first defogged image information; inputting the first defogged image information into the second image defogging network to obtain second defogged image information; and determining the first defogged image information and the second defogged image information as the at least one defogged image information.
According to one or more embodiments of the present disclosure, the second generating unit is further configured to: adding the first defogged image information, the second defogged image information and the matrix form of the reduced image in the at least one defogged image information according to elements to obtain an addition result; convolving the addition result to obtain a convolution result; and performing upsampling on the convolution result to obtain the defogged image.
According to one or more embodiments of the present disclosure, there is provided an electronic device including: one or more processors; a storage device having one or more programs stored thereon which, when executed by one or more processors, cause the one or more processors to implement a method as in any above.
According to one or more embodiments of the present disclosure, a computer-readable medium is provided, on which a computer program is stored, wherein the program, when executed by a processor, implements the method as any one of the above.
The foregoing description is only exemplary of the preferred embodiments of the disclosure and is illustrative of the principles of the technology employed. It will be appreciated by those skilled in the art that the scope of the invention in the embodiments of the present disclosure is not limited to the specific combination of the above-mentioned features, but also encompasses other embodiments in which any combination of the above-mentioned features or their equivalents is made without departing from the inventive concept as defined above. For example, the above features and (but not limited to) technical features with similar functions disclosed in the embodiments of the present disclosure are mutually replaced to form the technical solution.
Claims (10)
1. An image generation method, comprising:
down-sampling the original image to obtain a reduced image;
generating at least one defogged image information based on the reduced image;
generating a defogged image based on the at least one defogged image information and the reduced image.
2. The method of claim 1, wherein the generating at least one defogged image information based on the reduced image comprises:
and inputting the reduced image into an image defogging network to obtain the at least one piece of defogged image information.
3. The method of claim 2, wherein the image defogging network comprises: a first image defogging network and a second image defogging network; and
the inputting the reduced image into an image defogging network to obtain the at least one defogged image information includes:
inputting the reduced image into the first image defogging network to obtain first defogged image information;
inputting the first defogged image information into the second image defogging network to obtain second defogged image information;
determining the first defogged image information and the second defogged image information as the at least one defogged image information.
4. The method of claim 3, wherein the generating a defogged image based on the at least one defogged image information and the reduced image comprises:
adding the first defogged image information, the second defogged image information and the matrix form of the reduced image in the at least one defogged image information according to elements to obtain an addition result;
convolving the addition result to obtain a convolution result;
and performing upsampling on the convolution result to obtain the defogged image.
5. An image generation apparatus comprising:
a down-sampling unit configured to down-sample the original image to obtain a reduced image;
a first generating unit configured to generate at least one defogged image information based on the reduced image;
a second generating unit configured to generate a defogged image based on the at least one defogged image information and the reduced image.
6. The apparatus of claim 5, wherein the first generating unit is further configured to:
and inputting the reduced image into an image defogging network to obtain the at least one piece of defogged image information.
7. The apparatus of claim 6, wherein the image defogging network comprises: a first image defogging network and a second image defogging network; and
the first generation unit is further configured to:
inputting the reduced image into the first image defogging network to obtain first defogged image information;
inputting the first defogged image information into the second image defogging network to obtain second defogged image information;
determining the first defogged image information and the second defogged image information as the at least one defogged image information.
8. The apparatus of claim 7, the second generating unit further configured to:
adding the first defogged image information, the second defogged image information and the matrix form of the reduced image in the at least one defogged image information according to elements to obtain an addition result;
convolving the addition result to obtain a convolution result;
and performing upsampling on the convolution result to obtain the defogged image.
9. An electronic device, comprising:
one or more processors;
a storage device having one or more programs stored thereon,
when executed by the one or more processors, cause the one or more processors to implement the method of any one of claims 1-4.
10. A computer-readable medium, on which a computer program is stored, wherein the program, when executed by a processor, implements the method of any one of claims 1-4.
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